Federated CV Model for Distributed Edge Inferencing
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Solution Overview
Problem
Current computer vision systems face challenges in efficiently managing resources and providing consistent performance across diverse computing devices with varying resources, as they often require centralized processing and may not effectively utilize local hardware capabilities for inferencing tasks.
Innovation Solution
The implementation of a CV manager that generates a federated CV regression model, which initiates training on local hardware resource systems to create local models, and an enhanced networking interface for initial data processing and metadata generation, allowing for distributed processing and efficient resource configuration recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized processing is used to provide consistent performance across diverse computing devices, then performance consistency is improved, but device complexity and resource requirements increase
Solution Approach 1:
The patent segments the centralized processing architecture into distributed edge processing units deployed across multiple computing devices. Each device runs local processing models that handle inferencing tasks independently, eliminating the need for a single centralized processing system while maintaining performance consistency through standardized model deployment and federated learning techniques.
2Power
If centralized processing is used for computer vision inferencing, then processing power is improved, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent enables computing devices to perform self-service processing by deploying local processing models that utilize each device's own hardware resources (GPUs, TPUs, or other accelerators) for computer vision inferencing. This eliminates the need to transport data to centralized servers, allowing each device to independently execute inferencing tasks using its available computational resources, thereby improving both processing power and resource utilization efficiency simultaneously.
3Productivity
If local hardware resources are utilized for inferencing, then resource utilization efficiency is improved, but performance consistency across diverse devices deteriorates
Solution Approach 1:
The patent employs parameter changes by standardizing the processing models and their configuration parameters across diverse computing devices. Through federated learning, the system aggregates performance data from multiple devices and adjusts model parameters to optimize performance consistency. This allows local hardware resources to be utilized efficiently while maintaining standardized performance outcomes across devices with varying hardware capabilities.
Solution Approach 2:
The patent creates universal processing models that can execute across multiple types of hardware platforms (GPUs, TPUs, CPUs, or other accelerators). The processing models are designed with hardware-agnostic architectures and can be deployed on diverse computing devices with different local hardware resources. This universality ensures that performance consistency is maintained across devices while allowing each device to utilize its own hardware capabilities efficiently.
Data Source
AI summary
A system includes a processor, a processing system operatively connected to the processor, comprising a decoding-dedicated hardware component, an operating system operating on the processor and not on the processing system, wherein the processing system is programmed to obtain encoded data from a local data source, perform, using the decoding-dedicated hardware component, a decoding of the encoded data to obtain decoded data, perform a computer vision (CV) inferencing on the decoded data to obtain inferencing data, provide the inferencing data to the processor, and perform, by the processor, a remediation action based on the inferencing data.